1.1-1.4 sqoop概述及安装cdh版hadoop

一、概述

Sqoop是一个在结构化数据和Hadoop之间进行批量数据迁移的工具，结构化数据可以是Mysql、Oracle等RDBMS。
Sqoop底层用MapReduce程序实现抽取、转换、加载，MapReduce天生的特性保证了并行化和高容错率，而且
相比Kettle等传统ETL工具，任务跑在Hadoop集群上，减少了ETL服务器资源的使用情况。在特定场景下，抽取过程会有很大的性能提升。    如果要用Sqoop，必须正确安装并配置Hadoop，因依赖于本地的hadoop环境启动MR程序；mysql、oracle等数据库的JDBC驱动也要放到Sqoop的lib目录下。
本文针对的是Sqoop1，不涉及到Sqoop2，两者有很大区别；

import是把数据从RDBMS导入到Hadoop的工具；

二、安装hadoop

1、说明

sqoop与hadoop集成需要编译，比较麻烦，这里直接使用cdh版本的hadoop；

CDH安装包下载：http://archive.cloudera.com/cdh5/

使用cdh-5.3.6版本：

http://archive.cloudera.com/cdh5/cdh/5/

hadoop-2.5.0-cdh5.3.6.tar.gz
        hive-0.13.1-cdh5.3.6.tar.gz
       zookeeper-3.4.5-cdh5.3.6.tar.gz
       sqoop-1.4.5-cdh5.3.6.tar.gz

2、准备安装

#创建安装目录
[root@hadoop-senior opt]# mkdir /opt/cdh-5.3.6

#上传安装包
[root@hadoop-senior cdh]# pwd
/opt/softwares/cdh

[root@hadoop-senior cdh]# ls
 hadoop-2.5.0-cdh5.3.6.tar.gz  hive-0.13.1-cdh5.3.6.tar.gz  sqoop-1.4.5-cdh5.3.6.tar.gz

#解压hadoop、hive
[root@hadoop-senior cdh]# tar zxf hadoop-2.5.0-cdh5.3.6.tar.gz -C /opt/cdh-5.3.6/
[root@hadoop-senior cdh]# tar zxf hive-0.13.1-cdh5.3.6.tar.gz -C /opt/cdh-5.3.6/

#查看
[root@hadoop-senior cdh-5.3.6]# cd /opt/cdh-5.3.6/

[root@hadoop-senior cdh-5.3.6]# ls
hadoop-2.5.0-cdh5.3.6  hive-0.13.1-cdh5.3.6

[root@hadoop-senior cdh-5.3.6]# cd hadoop-2.5.0-cdh5.3.6/

[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# ls
bin  bin-mapreduce1  cloudera  etc  examples  examples-mapreduce1  include  lib  libexec  sbin  share  src

3、配置

java_home:

##/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/etc/hadoop/hadoop-env.sh
export JAVA_HOME=/opt/modules/jdk1.7.0_80

##/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/etc/hadoop/yarn-env.sh
export JAVA_HOME=/opt/modules/jdk1.7.0_80

##/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/etc/hadoop/mapred-env.sh
export JAVA_HOME=/opt/modules/jdk1.7.0_80

core-site.xml

##/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/etc/hadoop/core-site.xml
<configuration>

    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://hadoop-senior.ibeifeng.com:8020</value>
    </property>

    <property>
        <name>hadoop.tmp.dir</name>
        <value>/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/data/tmp</value>
     </property>

</configuration>

[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# mkdir -pv /opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/data/tmp

hdfs-site.xml

##/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/etc/hadoop/hdfs-site.xml
<configuration>
    <property>
        <name>dfs.namenode.secondary.http-address</name>
        <value>hadoop-senior.ibeifeng.com:50090</value>
    </property>

    <property>
        <name>dfs.namenode.http-address</name>
        <value>hadoop-senior.ibeifeng.com:50070</value>
    </property>

    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>

    <property>
        <name>dfs.permissions</name>
        <value>false</value>
    </property>

</configuration>

slaves文件

##/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/etc/hadoop/slaves

hadoop-senior.ibeifeng.com

yarn-site.xml

##/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/etc/hadoop/yarn-site.xml
<configuration>

    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>

    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value>hadoop-senior.ibeifeng.com</value>
    </property>

    <property>
        <name>yarn.nodemanager.resource.cpu-vcores</name>
        <value>4</value>
    </property>

    <property>
        <name>yarn.log-aggregation-enable</name>
        <value>true</value>
    </property>

    <property>
        <name>yarn.log-aggregation.retain-seconds</name>
        <value>604800</value>
    </property>

</configuration>

mapred-site.xml

##/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6/etc/hadoop/mapred-site.xml         //先重命名
<configuration>

    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>

    <property>
        <name>mapreduce.jobhistory.address</name>
        <value>hadoop-senior.ibeifeng.com:10020</value>
    </property>

    <property>
        <name>mapreduce.jobhistory.webapp.address</name>
        <value>hadoop-senior.ibeifeng.com:19888</value>
    </property>

</configuration>

3、启动

#格式化文件系统
[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# bin/hdfs namenode -format

[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# rm -rf /tmp/*

#启动hdfs
[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# sbin/hadoop-daemon.sh start namenode
[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# sbin/hadoop-daemon.sh start datanode

#启动yarn
[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# sbin/yarn-daemon.sh start resourcemanager
[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# sbin/yarn-daemon.sh start nodemanager

#启动historyserver
[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# sbin/mr-jobhistory-daemon.sh start historyserver

#查看
[root@hadoop-senior ~]# jps
3165 Jps
3119 JobHistoryServer
2683 ResourceManager
2573 DataNode
2471 NameNode
2960 NodeManager

三、安装hive

1、/opt/cdh-5.3.6/hive-0.13.1-cdh5.3.6/conf/hive-env.sh           //先重命名

HADOOP_HOME=/opt/cdh-5.3.6/hadoop-2.5.0-cdh5.3.6

export HIVE_CONF_DIR=/opt/cdh-5.3.6/hive-0.13.1-cdh5.3.6/conf

2、/opt/cdh-5.3.6/hive-0.13.1-cdh5.3.6/conf/hive-log4j.properties     //先重命名

hive.log.dir=/opt/cdh-5.3.6/hive-0.13.1-cdh5.3.6/logs

3、创建配置文件

[root@hadoop-senior ~]# cd /opt/cdh-5.3.6/hive-0.13.1-cdh5.3.6/conf/

[root@hadoop-senior conf]# touch hive-site.xml

################hive-site.xml#######################
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>

    <property>
        <name>javax.jdo.option.ConnectionURL</name>
        <value>jdbc:mysql://hadoop-senior.ibeifeng.com:3306/metadata?createDatabaseIfNotExist=true</value>
        <description>JDBC connect string for a JDBC metastore</description>
    </property>

    <property>
        <name>javax.jdo.option.ConnectionDriverName</name>
        <value>com.mysql.jdbc.Driver</value>
        <description>Driver class name for a JDBC metastore</description>
    </property>

    <property>
        <name>javax.jdo.option.ConnectionUserName</name>
        <value>root</value>
        <description>username to use against metastore database</description>
    </property>

    <property>
        <name>javax.jdo.option.ConnectionPassword</name>
        <value>123456</value>
        <description>password to use against metastore database</description>
    </property>

    <property>
        <name>hive.cli.print.header</name>
        <value>true</value>
        <description>Whether to print the names of the columns in query output.</description>
    </property>

    <property>
        <name>hive.cli.print.current.db</name>
        <value>true</value>
        <description>Whether to include the current database in the Hive prompt.</description>
    </property>

    <property>
        <name>hive.fetch.task.conversion</name>
        <value>more</value>
        </property>

</configuration>

4、拷贝mysql驱动文件

[root@hadoop-senior hive-0.13.1-cdh5.3.6]# cp /opt/modules/hive-0.13.1/lib/mysql-connector-java-5.1.27-bin.jar ./lib/

5、连接hive

##连接测试
[root@hadoop-senior hive-0.13.1-cdh5.3.6]# bin/hive
Logging initialized using configuration in file:/opt/cdh-5.3.6/hive-0.13.1-cdh5.3.6/conf/hive-log4j.properties
hive (default)>

##创建hive的数据存储目录
[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# bin/hdfs dfs -mkdir -p /user/hive/warehouse

[root@hadoop-senior hadoop-2.5.0-cdh5.3.6]# bin/hdfs dfs -chmod g+w /user/hive/warehouse

##创建一张测试表
hive (default)> create table student(id int, name string) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
OK
Time taken: 0.708 seconds

hive (default)> load data local inpath '/opt/datas/student.txt' overwrite into table student;
Loading data to table default.student
Table default.student stats: [numFiles=1, numRows=0, totalSize=36, rawDataSize=0]
OK
Time taken: 0.885 seconds

hive (default)> select * from student;
OK
student.id    student.name
1001    zhangsan
1002    lisi
1003    wangwu
Time taken: 0.218 seconds, Fetched: 3 row(s)

此时web页面应该也可以打开：ip:8088     ip:50070